Bryon Aragam - RankMe

90 papers A* 27A 7Journal 54Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Beyond identifiability: Learning causal representations with few environments and finite samples. CoRR Inbeom Lee, Tongtong Jin, Bryon Aragam
2025	J	`jnl`	Differentiable Structure Learning for General Binary Data. CoRR Chang Deng, Bryon Aragam
2025	A*	`conf`	Dimension-Independent Rates for Structured Neural Density Estimation. ICML Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
2025	J	`jnl`	Intervening to learn and compose disentangled representations. CoRR Alex Markham, Jeri A. Chang, Isaac Hirsch, Liam Solus, Bryon Aragam
2025	J	`jnl`	ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization. CoRR Yinjie Wang, Ling Yang, Guohao Li, Mengdi Wang, Bryon Aragam
2025	J	`jnl`	Towards Interpretable Deep Generative Models via Causal Representation Learning. CoRR Gemma E. Moran, Bryon Aragam
2024	A*	`conf`	Breaking the curse of dimensionality in structured density estimation. NeurIPS Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
2024	J	`jnl`	Breaking the curse of dimensionality in structured density estimation. CoRR Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
2024	J	`jnl`	Dimension-independent rates for structured neural density estimation. CoRR Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
2024	A*	`conf`	Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. NeurIPS Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2024	J	`jnl`	Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. CoRR Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2024	A*	`conf`	From Causal to Concept-Based Representation Learning. NeurIPS Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
2024	J	`jnl`	Greedy equivalence search for nonparametric graphical models. CoRR Bryon Aragam
2024	A*	`conf`	Identifying General Mechanism Shifts in Linear Causal Representations. NeurIPS Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam, Pradeep Ravikumar
2024	J	`jnl`	Identifying General Mechanism Shifts in Linear Causal Representations. CoRR Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam, Pradeep Ravikumar
2024	A	`conf`	Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models. AISTATS Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam
2024	J	`jnl`	Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. CoRR Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
2024	J	`jnl`	Likelihood-based Differentiable Structure Learning. CoRR Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam
2024	A*	`conf`	Markov Equivalence and Consistency in Differentiable Structure Learning. NeurIPS Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam
2024	A*	`conf`	On the Origins of Linear Representations in Large Language Models. ICML Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam, Victor Veitch
2024	J	`jnl`	On the Origins of Linear Representations in Large Language Models. CoRR Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam, Victor Veitch
2024	A	`conf`	Optimal estimation of Gaussian (poly)trees. AISTATS Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya
2024	J	`jnl`	Optimal estimation of Gaussian (poly)trees. CoRR Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya
2023	A*	`conf`	Assumption violations in causal discovery and the robustness of score matching. NeurIPS Francesco Montagna, Atalanti-Anastasia Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello
2023	J	`jnl`	Assumption violations in causal discovery and the robustness of score matching. CoRR Francesco Montagna, Atalanti-Anastasia Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello
2023	A*	`conf`	Global Optimality in Bivariate Gradient-based DAG Learning. NeurIPS Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam
2023	J	`jnl`	Global Optimality in Bivariate Gradient-based DAG Learning. CoRR Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
2023	J	`jnl`	Inconsistency of cross-validation for structure learning in Gaussian graphical models. CoRR Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam
2023	A*	`conf`	Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. NeurIPS Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
2023	J	`jnl`	Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. CoRR Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
2023	A*	`conf`	Learning Mixtures of Gaussians with Censored Data. ICML Wai Ming Tai, Bryon Aragam
2023	J	`jnl`	Learning Mixtures of Gaussians with Censored Data. CoRR Wai Ming Tai, Bryon Aragam
2023	A*	`conf`	Learning Nonparametric Latent Causal Graphs with Unknown Interventions. NeurIPS Yibo Jiang, Bryon Aragam
2023	J	`jnl`	Learning nonparametric latent causal graphs with unknown interventions. CoRR Yibo Jiang, Bryon Aragam
2023	J	`jnl`	Neuro-Causal Factor Analysis. CoRR Alex Markham, Mingyu Liu, Bryon Aragam, Liam Solus
2023	—	`ch.`	Neuro-Causal Models. Compendium of Neurosymbolic Artificial Intelligence Bryon Aragam, Pradeep Ravikumar
2023	A*	`conf`	Optimizing NOTEARS Objectives via Topological Swaps. ICML Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Kumar Ravikumar
2023	J	`jnl`	Optimizing NOTEARS Objectives via Topological Swaps. CoRR Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
2023	A*	`conf`	Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures. COLT Wai Ming Tai, Bryon Aragam
2023	A*	`conf`	Uncovering Meanings of Embeddings via Partial Orthogonality. NeurIPS Yibo Jiang, Bryon Aragam, Victor Veitch
2023	J	`jnl`	Uncovering Meanings of Embeddings via Partial Orthogonality. CoRR Yibo Jiang, Bryon Aragam, Victor Veitch
2023	A*	`conf`	iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. NeurIPS Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
2023	J	`jnl`	iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. CoRR Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
2022	J	`jnl`	A non-graphical representation of conditional independence via the neighbourhood lattice. CoRR Arash A. Amini, Bryon Aragam, Qing Zhou
2022	J	`jnl`	A super-polynomial lower bound for learning nonparametric mixtures. CoRR Bryon Aragam, Wai Ming Tai
2022	A*	`conf`	DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. NeurIPS Kevin Bello, Bryon Aragam, Pradeep Ravikumar
2022	J	`jnl`	DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. CoRR Kevin Bello, Bryon Aragam, Pradeep Ravikumar
2022	J	`jnl`	Fundamental Limits and Tradeoffs in Invariant Representation Learning. J. Mach. Learn. Res. Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar
2022	J	`jnl`	Identifiability of deep generative models under mixture priors without auxiliary information. CoRR Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2022	A*	`conf`	Identifiability of deep generative models without auxiliary information. NeurIPS Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2022	A	`conf`	On perfectness in Gaussian graphical models. AISTATS Arash A. Amini, Bryon Aragam, Qing Zhou
2022	A	`conf`	Optimal estimation of Gaussian DAG models. AISTATS Ming Gao, Wai Ming Tai, Bryon Aragam
2022	J	`jnl`	Optimal estimation of Gaussian DAG models. CoRR Ming Gao, Wai Ming Tai, Bryon Aragam
2022	J	`jnl`	Trade-offs of Linear Mixed Models in Genome-Wide Association Studies. J. Comput. Biol. Haohan Wang, Bryon Aragam, Eric P. Xing
2021	A*	`conf`	Efficient Bayesian network structure learning via local Markov boundary search. NeurIPS Ming Gao, Bryon Aragam
2021	J	`jnl`	Efficient Bayesian network structure learning via local Markov boundary search. CoRR Ming Gao, Bryon Aragam
2021	A*	`conf`	Learning latent causal graphs via mixture oracles. NeurIPS Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2021	J	`jnl`	Learning latent causal graphs via mixture oracles. CoRR Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2021	J	`jnl`	NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters. CoRR Benjamin J. Lengerich, Caleb Ellington, Bryon Aragam, Eric P. Xing, Manolis Kellis
2021	A*	`conf`	Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families. NeurIPS Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam
2021	J	`jnl`	Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families. CoRR Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam
2021	J	`jnl`	Tradeoffs of Linear Mixed Models in Genome-wide Association Studies. CoRR Haohan Wang, Bryon Aragam, Eric P. Xing
2020	A*	`conf`	A polynomial-time algorithm for learning nonparametric causal graphs. NeurIPS Ming Gao, Yi Ding, Bryon Aragam
2020	J	`jnl`	A polynomial-time algorithm for learning nonparametric causal graphs. CoRR Ming Gao, Yi Ding, Bryon Aragam
2020	A	`conf`	Automated Dependence Plots. UAI David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar
2020	A	`conf`	DYNOTEARS: Structure Learning from Time-Series Data. AISTATS Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, Bryon Aragam
2020	J	`jnl`	DYNOTEARS: Structure Learning from Time-Series Data. CoRR Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Paul Beaumont, Konstantinos Georgatzis, Bryon Aragam
2020	J	`jnl`	Fundamental Limits and Tradeoffs in Invariant Representation Learning. CoRR Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar
2020	A	`conf`	Learning Sparse Nonparametric DAGs. AISTATS Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2019	J	`jnl`	Diagnostic Curves for Black Box Models. CoRR David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar
2019	A*	`conf`	Fault Tolerance in Iterative-Convergent Machine Learning. ICML Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric P. Xing
2019	A*	`conf`	Globally optimal score-based learning of directed acyclic graphs in high-dimensions. NeurIPS Bryon Aragam, Arash A. Amini, Qing Zhou
2019	A*	`conf`	Learning Sample-Specific Models with Low-Rank Personalized Regression. NeurIPS Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing
2019	J	`jnl`	Learning Sample-Specific Models with Low-Rank Personalized Regression. CoRR Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing
2019	J	`jnl`	Learning Sparse Nonparametric DAGs. CoRR Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2019	J	`jnl`	On perfectness in Gaussian graphical models. CoRR Arash A. Amini, Bryon Aragam, Qing Zhou
2019	J	`jnl`	Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data. Bioinform. Haohan Wang, Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing
2018	A*	`conf`	DAGs with NO TEARS: Continuous Optimization for Structure Learning. NeurIPS Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2018	J	`jnl`	DAGs with NO TEARS: Smooth Optimization for Structure Learning. CoRR Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2018	J	`jnl`	Fault Tolerance in Iterative-Convergent Machine Learning. CoRR Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric P. Xing
2018	J	`jnl`	Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering. CoRR Bryon Aragam, Chen Dan, Pradeep Ravikumar, Eric P. Xing
2018	J	`jnl`	Personalized regression enables sample-specific pan-cancer analysis. Bioinform. Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing
2018	J	`jnl`	Sample Complexity of Nonparametric Semi-Supervised Learning. CoRR Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2018	A*	`conf`	The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. NeurIPS Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2017	J	`jnl`	Learning Large-Scale Bayesian Networks with the sparsebn Package. CoRR Bryon Aragam, Jiaying Gu, Qing Zhou
2017	J	`jnl`	Partial correlation graphs and the neighborhood lattice. CoRR Arash A. Amini, Bryon Aragam, Qing Zhou
2017	—	`conf`	Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies. BIBM Haohan Wang, Bryon Aragam, Eric P. Xing
2015	J	`jnl`	Concave penalized estimation of sparse Gaussian Bayesian networks. J. Mach. Learn. Res. Bryon Aragam, Qing Zhou
2015	J	`jnl`	Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression. CoRR Bryon Aragam, Arash A. Amini, Qing Zhou
2014	J	`jnl`	Concave Penalized Estimation of Sparse Bayesian Networks. CoRR Bryon Aragam, Qing Zhou